Efficient object tracking algorithm based on lightweight Siamese networks

The increasing popularity of mobile applications has catalyzed advancements in object tracking algorithms for mobile platforms. This paper introduces a lightweight object tracking algorithm, designed for real-time and accurate tracking on mobile devices. We propose a feature extraction network with a quantization-friendly architecture and a novel mask-based padding method to enhance tracking precision. By refining MobileNetV3's structure, we mitigate accuracy degradation during quantization and reduce computational demands. The proposed padding technique addresses the outlier generation issue common in traditional padding methods, thereby enhancing positional accuracy. Additionally, an extent adjustment module is incorporated to facilitate prompt error correction during suboptimal tracking. Comprehensive evaluations on OTB50, OTB100, VOT2018, UAV123, and LaSOT demonstrate the superiority of our method over several state-of-the-art trackers in both speed and accuracy. Notably, our feature extraction model is only 3.4 MB, running at 243fps on an RTX 3090 GPU, and can adapt to various sizes of video inputs.